השוואת שיטות

	דגימה שיטתית מבוססת שדה	דגימת אשכולות אדפטיבית (Adaptive Cluster Sampling
תחום	מתודולוגיית סקרים	מתודולוגיית סקרים
משפחה	Process / pipeline	Process / pipeline
שנת המקור	1940s–1950s (systematic sampling foundations); field adaptations consolidated by 1970s	1990
הוגה השיטה	William G. Cochran (systematic sampling foundations); adapted to field contexts in ecological and agricultural survey literature	Steven K. Thompson
סוג	Probability sampling design	Probability-based adaptive sampling design
מקור מכונן	Cochran, W. G. (1977). Sampling Techniques (3rd ed.). John Wiley & Sons. ISBN: 978-0471162407	Thompson, S. K. (1990). Adaptive cluster sampling. Journal of the American Statistical Association, 85(412), 1050–1059.
כינויים	systematic field sampling, grid-based field sampling, regular interval field sampling	ACS, adaptive network sampling, sequential cluster sampling, neighborhood adaptive sampling
קשורות	6	6
תקציר	Field-based systematic sampling applies systematic (regular-interval) selection to real-world field environments — plots of land, transects, geographic grids, or physical survey routes. A random starting point is chosen, then every k-th unit or location is sampled at equal spatial or sequential intervals. Widely used in ecology, agriculture, environmental science, and field surveys, it delivers spatially even coverage at low operational cost while maintaining probability-sampling properties.	Adaptive cluster sampling (ACS) is a probability-based design in which an initial random sample of units triggers the inclusion of neighboring units whenever a predefined condition — typically a threshold count of a rare attribute — is satisfied. Developed by Steven K. Thompson in 1990, ACS is especially powerful for estimating the abundance or distribution of rare, spatially clustered populations such as endangered species, disease hotspots, or hard-to-reach social groups.